class_docstr: Given wrapped example, e.g. A fun movie ! it is <mask> and <mask> .
  The input tokens is:  A fun movie ! it is <extra_id_0> and <extra_id_1> . </s>
  The decoder tokens is: <pad> <extra_id_0> <extra_id_1> </s>
  The expected output is <extra_id_0> good interesting </s>
code: def truncate_decoder_inputs(self, inputs):
      if self.decode_from_pad:
          inputs["decoder_input_ids"].insert(
              0, self.tokenizer.pad_token_id
          )
          inputs["loss_ids"].insert(0, 0)
  
      for key in inputs:
          inputs[key] = inputs[key][: self.decoder_max_length - 1]
  
      if self.predict_eos:
          inputs["decoder_input_ids"].append(
              self.tokenizer.eos_token_id
          )
          inputs["loss_ids"].append(1)
      inputs = self.padding(
          inputs,
          max_len=self.decoder_max_length,
          pad_id_for_inputs=self.tokenizer.pad_token_id,
      )
      return inputs
  
imports: from json import decoder
  from transformers.tokenization_utils import PreTrainedTokenizer
  from openprompt.plms.utils import TokenizerWrapper
  from typing import List, Dict, Optional
  from collections import defaultdict
  from openprompt.utils.logging import logger
label_desc: Use image node_id 6 for calling the T5TokenizerWrapper obj's underlying member method code with example usage: obj.truncate_decoder_inputs(inputs) and returns: inputs